2017
DOI: 10.1364/ao.56.003952
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On the discrimination of multiple phytoplankton groups from light absorption spectra of assemblages with mixed taxonomic composition and variable light conditions

Abstract: According to recommendations of the international community of phytoplankton functional type algorithm devel- 20Plasticity of absorption spectra due to changes in light conditions weakly affects interspecific differences, with errors 21 <21% for retrievals of pigment concentrations from mixed assemblages.

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Cited by 25 publications
(23 citation statements)
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“…The optical properties of mixed species must be taken into consideration; 3. Light, nutrition, and related growth parameters should be connected with optical properties and finally incorporated into the simulation dataset [80]. Second, many factors affect the signal transmission from natural waters to the satellite sensor and much work needs to be done; for example, spectral classification is an effective method for deriving useful information [81], accurate atmospheric correction is also an important research Following the transfer learning method, we first used the in situ dataset to update the last layers in the corresponding DNN, which transitions the species information from general to specific as the number of layers increases, and then obtained predictions for phytoplankton species and associated compositions in natural waters.…”
Section: Potentials and Limitationsmentioning
confidence: 99%
“…The optical properties of mixed species must be taken into consideration; 3. Light, nutrition, and related growth parameters should be connected with optical properties and finally incorporated into the simulation dataset [80]. Second, many factors affect the signal transmission from natural waters to the satellite sensor and much work needs to be done; for example, spectral classification is an effective method for deriving useful information [81], accurate atmospheric correction is also an important research Following the transfer learning method, we first used the in situ dataset to update the last layers in the corresponding DNN, which transitions the species information from general to specific as the number of layers increases, and then obtained predictions for phytoplankton species and associated compositions in natural waters.…”
Section: Potentials and Limitationsmentioning
confidence: 99%
“…With hyperspectral data, several researchers have independently demonstrated with field and laboratory data that mixtures of major PTs (e.g., diatoms, Prochlorococcus [cyanobacteria], coccolithophores) can be differentiated for members contributing largely total chlorophyll a (Bracher et al, 2009;Xi et al, 2015;Organelli et al, 2017;Catlett et al, 2018). Multispectral imagery can only capture the average trends in the open ocean related to dominant PGs (Figure 1).…”
Section: What Phytoplankton Metrics Can Be Linked To Hyperspectral Immentioning
confidence: 99%
“…They have not been demonstrated to be unique to the specific taxa and can be based on pigment-specific features that span different phytoplankton groups. Differentiating dinoflagellates and diatoms globally may be extremely challenging because they exhibit similar spectral absorption and large intraspecies variability (Organelli et al, 2017;Catlett and Siegel, 2018). Hence, a user cannot simply apply these approaches to identify specific taxa widely across different aquatic ecosystems.…”
Section: What Phytoplankton Metrics Can Be Linked To Hyperspectral Immentioning
confidence: 99%
“…It is clear that the case studies reflect simplified representative examples of much wider pigmentand size-related variability in nature, but the described dependence of absorption-driven pigment signals versus scattering-driven cell size signals on biomass holds across assemblage types. Optical PFT effects are most easily identified in relatively high biomass environments (Chl a > 1 mg·m 3 ) [36][37][38], and where the IOP budget is dominated by phytoplankton [37,39], and so the case studies deal with these water types. However, as the sensitivity analysis shows, together with the contextual discussion around ambiguity and uncertainty in satellite R rs , the conclusions of this study have implications for the identification of PFT changes from satellite R rs across all water types.…”
Section: Study Objectives and Outlinementioning
confidence: 99%
“…The main light-harvesting pigments in typical diatom and dinoflagellate assemblages (fucoxanthin and peridinin, respectively)-while chemotaxonomically distinct-display the typical broad, featureless absorption spectra characteristic of carotenoids, with peaks centered around 500 nm [87] and vary well within the natural variability of phytoplankton absorption ( Figure A1). They consequently have similar refractive indices [27] and so these types were combined into a generalised set of diatom/dinoflagellate IOPs, as no significant difference was found between the dinoflagellate and diatom groups in terms of their optics that could not be attributed to the respective particle sizes (see also [38,88]). This group of IOPs should correctly be referred to as Chl a-carotenoid IOPs.…”
Section: Appendix a Phytoplankton Assemblage Variability In The Eap mentioning
confidence: 99%